MistralEmbedder class is used to embed text data into vectors using the Mistral API. Get your key from here.
Usage
cookbook/embedders/mistral_embedder.py
from phi.agent import AgentKnowledge
from phi.vectordb.pgvector import PgVector
from phi.embedder.mistral import MistralEmbedder
embeddings = MistralEmbedder().get_embedding("The quick brown fox jumps over the lazy dog.")
# Print the embeddings and their dimensions
print(f"Embeddings: {embeddings[:5]}")
print(f"Dimensions: {len(embeddings)}")
# Example usage:
knowledge_base = AgentKnowledge(
vector_db=PgVector(
db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
table_name="mistral_embeddings",
embedder=MistralEmbedder(),
),
num_documents=2,
)
Params
| Parameter | Type | Default | Description |
|---|---|---|---|
model | str | "mistral-embed" | The name of the model used for generating embeddings. |
dimensions | int | 1024 | The dimensionality of the embeddings generated by the model. |
request_params | Optional[Dict[str, Any]] | - | Additional parameters to include in the API request. Optional. |
api_key | str | - | The API key used for authenticating requests. |
endpoint | str | - | The endpoint URL for the API requests. |
max_retries | Optional[int] | - | The maximum number of retries for API requests. Optional. |
timeout | Optional[int] | - | The timeout duration for API requests. Optional. |
client_params | Optional[Dict[str, Any]] | - | Additional parameters for configuring the API client. Optional. |
mistral_client | Optional[MistralClient] | - | An instance of the MistralClient to use for making API requests. Optional. |